Computational Structural Biology: Successes, Future Directions, and Challenges

• Main Authors: Ruth Nussinov, Chung-Jung Tsai, Amarda Shehu, Hyunbum Jang
• Format: Article
• Language: English
• Published: MDPI AG 2019-02-01
• Series: Molecules
• Subjects: big data; machine intelligence; bioinformatics; biological modeling; free-energy landscape; mutations
• Online Access: https://www.mdpi.com/1420-3049/24/3/637

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous ‘big data’ integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells’ actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.